General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Executive Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • E-mail:
    • Journal Metrics:

Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2022 Vol.14(2): 84-88 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2022.V14.1314

Assessment of Probability Defaults Using K-Means Based Multinomial Logistic Regression

G. Arutjothi and C. Senthamarai

Abstract—Classification analysis is a key and easy tool in machine learning and prediction. Because of the large amount of data and the need to convert this data into useful information and knowledge, machine learning has gotten a lot of attention in the information industry and also in society because of the large amount of data and the issues that come with it. In this paper, a K-Means based Multinomial Logistic Regression (MLR) prediction algorithm is used for evaluating the performance of Probability Defaults (PD), and suggestions are made to improve financial status. The necessary information about the members of PD has been collected from the UCI machine learning repository. The parameters are chosen for the study using the feature selection method. The research goal is to find default risk probabilities and they are assessed by accuracy, RMSE (Root Mean Squared Error), error rate, and time. K-means based Multinomial Logistic Regression (MLR) significantly outperforms other classifier models. Assessment of PD will have an impact on the financial industry.

Index Terms—K-means, MLR, Probability Defaults (PD), default classification, classifier models.

G. Arutjothi and C. Senthamarai are with Department of Computer Applications, Govt. Arts College (Autonomous), Salem-7, TamilNadu, India (e-mail:,


Cite:G. Arutjothi and C. Senthamarai, "Assessment of Probability Defaults Using K-Means Based Multinomial Logistic Regression," International Journal of Computer Theory and Engineering vol. 14, no. 2, pp. 84-88, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Copyright © 2008-2023. International Association of Computer Science and Information Technology. All rights reserved.